Load Data
dataset <- read.delim("raw_data/Figure5H.txt", stringsAsFactors = FALSE)
dataset$genotype <- gsub("Δ","d",gsub(" ", "", dataset$genotype))
dataset$genotype <- factor(dataset$genotype)
dataset$Experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each=length(unique(dataset$genotype))))
dataset$BRCA <- factor(gsub("\\+.*","",dataset$genotype))
dataset$Alc1 <- factor(gsub(".*[T,1]\\+","",dataset$genotype))
dataset$UID <- factor(paste(dataset$Experiment, dataset$Alc1, dataset$BRCA))
dataset$GSID <- factor(paste(dataset$Alc1, dataset$BRCA))
# wide format
kable(dataset, row.names = F)
| h/d11+GFP |
2850 |
2670 |
1956 |
1980 |
exp1 |
h/d11 |
GFP |
exp1 GFP h/d11 |
GFP h/d11 |
| h/d11+GFP-ALC1 |
2600 |
2320 |
2150 |
2010 |
exp1 |
h/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
| d11/d11+GFP |
1576 |
1296 |
660 |
109 |
exp1 |
d11/d11 |
GFP |
exp1 GFP d11/d11 |
GFP d11/d11 |
| d11/d11+GFP-ALC1 |
1010 |
880 |
550 |
360 |
exp1 |
d11/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
| h/d11+GFP |
2620 |
2570 |
1675 |
1480 |
exp2 |
h/d11 |
GFP |
exp2 GFP h/d11 |
GFP h/d11 |
| h/d11+GFP-ALC1 |
2450 |
2120 |
1980 |
1840 |
exp2 |
h/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
| d11/d11+GFP |
1740 |
1300 |
740 |
95 |
exp2 |
d11/d11 |
GFP |
exp2 GFP d11/d11 |
GFP d11/d11 |
| d11/d11+GFP-ALC1 |
920 |
820 |
520 |
220 |
exp2 |
d11/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
| h/d11+GFP |
2356 |
2184 |
1586 |
1148 |
exp3 |
h/d11 |
GFP |
exp3 GFP h/d11 |
GFP h/d11 |
| h/d11+GFP-ALC1 |
2192 |
2066 |
1760 |
1518 |
exp3 |
h/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
| d11/d11+GFP |
840 |
680 |
440 |
180 |
exp3 |
d11/d11 |
GFP |
exp3 GFP d11/d11 |
GFP d11/d11 |
| d11/d11+GFP-ALC1 |
396 |
356 |
288 |
140 |
exp3 |
d11/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
| h/d11+GFP |
2256 |
2052 |
1552 |
1020 |
exp4 |
h/d11 |
GFP |
exp4 GFP h/d11 |
GFP h/d11 |
| h/d11+GFP-ALC1 |
2260 |
2120 |
1850 |
1314 |
exp4 |
h/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
| d11/d11+GFP |
816 |
632 |
340 |
152 |
exp4 |
d11/d11 |
GFP |
exp4 GFP d11/d11 |
GFP d11/d11 |
| d11/d11+GFP-ALC1 |
528 |
482 |
284 |
211 |
exp4 |
d11/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "Treatment", value.name = "Counts")
dataset$Alc1 <- relevel(dataset$Alc1, ref = "GFP")
dataset$BRCA <- relevel(dataset$BRCA, ref = "h/d11")
dataset$UID <- relevel(dataset$UID, ref = "exp1 GFP h/d11")
dataset$Olaparib <- gsub("NT","1",dataset$Treatment)
dataset$Olaparib <- gsub("olaparib_|nM","",dataset$Olaparib)
dataset$Olaparib <- log10(as.integer(dataset$Olaparib))
dataset$Offset <- NA
for(uid in levels(dataset$UID)){
dataset$Offset[dataset$UID == uid] <- mean(dataset$Counts[dataset$UID == uid])
}
dataset$NormCounts <- dataset$Counts / dataset$Offset
dataset$Offset2 <- NA
for(gsid in levels(dataset$GSID)){
dataset$Offset2[dataset$GSID == gsid] <- mean(dataset$NormCounts[dataset$GSID == gsid & dataset$Olaparib == 0])
}
dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2
# long format
kable(dataset, row.names = F)
| h/d11+GFP |
exp1 |
h/d11 |
GFP |
exp1 GFP h/d11 |
GFP h/d11 |
NT |
2850 |
0.000000 |
2364.00 |
1.2055838 |
1.267157 |
0.9514086 |
| h/d11+GFP-ALC1 |
exp1 |
h/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
NT |
2600 |
0.000000 |
2270.00 |
1.1453744 |
1.168804 |
0.9799541 |
| d11/d11+GFP |
exp1 |
d11/d11 |
GFP |
exp1 GFP d11/d11 |
GFP d11/d11 |
NT |
1576 |
0.000000 |
910.25 |
1.7313925 |
1.695022 |
1.0214570 |
| d11/d11+GFP-ALC1 |
exp1 |
d11/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
NT |
1010 |
0.000000 |
700.00 |
1.4428571 |
1.418106 |
1.0174538 |
| h/d11+GFP |
exp2 |
h/d11 |
GFP |
exp2 GFP h/d11 |
GFP h/d11 |
NT |
2620 |
0.000000 |
2086.25 |
1.2558418 |
1.267157 |
0.9910707 |
| h/d11+GFP-ALC1 |
exp2 |
h/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
NT |
2450 |
0.000000 |
2097.50 |
1.1680572 |
1.168804 |
0.9993609 |
| d11/d11+GFP |
exp2 |
d11/d11 |
GFP |
exp2 GFP d11/d11 |
GFP d11/d11 |
NT |
1740 |
0.000000 |
968.75 |
1.7961290 |
1.695022 |
1.0596492 |
| d11/d11+GFP-ALC1 |
exp2 |
d11/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
NT |
920 |
0.000000 |
620.00 |
1.4838710 |
1.418106 |
1.0463754 |
| h/d11+GFP |
exp3 |
h/d11 |
GFP |
exp3 GFP h/d11 |
GFP h/d11 |
NT |
2356 |
0.000000 |
1818.50 |
1.2955733 |
1.267157 |
1.0224255 |
| h/d11+GFP-ALC1 |
exp3 |
h/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
NT |
2192 |
0.000000 |
1884.00 |
1.1634820 |
1.168804 |
0.9954464 |
| d11/d11+GFP |
exp3 |
d11/d11 |
GFP |
exp3 GFP d11/d11 |
GFP d11/d11 |
NT |
840 |
0.000000 |
535.00 |
1.5700935 |
1.695022 |
0.9262966 |
| d11/d11+GFP-ALC1 |
exp3 |
d11/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
NT |
396 |
0.000000 |
295.00 |
1.3423729 |
1.418106 |
0.9465957 |
| h/d11+GFP |
exp4 |
h/d11 |
GFP |
exp4 GFP h/d11 |
GFP h/d11 |
NT |
2256 |
0.000000 |
1720.00 |
1.3116279 |
1.267157 |
1.0350953 |
| h/d11+GFP-ALC1 |
exp4 |
h/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
NT |
2260 |
0.000000 |
1886.00 |
1.1983033 |
1.168804 |
1.0252387 |
| d11/d11+GFP |
exp4 |
d11/d11 |
GFP |
exp4 GFP d11/d11 |
GFP d11/d11 |
NT |
816 |
0.000000 |
485.00 |
1.6824742 |
1.695022 |
0.9925971 |
| d11/d11+GFP-ALC1 |
exp4 |
d11/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
NT |
528 |
0.000000 |
376.25 |
1.4033223 |
1.418106 |
0.9895751 |
| h/d11+GFP |
exp1 |
h/d11 |
GFP |
exp1 GFP h/d11 |
GFP h/d11 |
olaparib_30nM |
2670 |
1.477121 |
2364.00 |
1.1294416 |
1.267157 |
0.8913196 |
| h/d11+GFP-ALC1 |
exp1 |
h/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_30nM |
2320 |
1.477121 |
2270.00 |
1.0220264 |
1.168804 |
0.8744205 |
| d11/d11+GFP |
exp1 |
d11/d11 |
GFP |
exp1 GFP d11/d11 |
GFP d11/d11 |
olaparib_30nM |
1296 |
1.477121 |
910.25 |
1.4237847 |
1.695022 |
0.8399799 |
| d11/d11+GFP-ALC1 |
exp1 |
d11/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_30nM |
880 |
1.477121 |
700.00 |
1.2571429 |
1.418106 |
0.8864944 |
| h/d11+GFP |
exp2 |
h/d11 |
GFP |
exp2 GFP h/d11 |
GFP h/d11 |
olaparib_30nM |
2570 |
1.477121 |
2086.25 |
1.2318754 |
1.267157 |
0.9721571 |
| h/d11+GFP-ALC1 |
exp2 |
h/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_30nM |
2120 |
1.477121 |
2097.50 |
1.0107271 |
1.168804 |
0.8647531 |
| d11/d11+GFP |
exp2 |
d11/d11 |
GFP |
exp2 GFP d11/d11 |
GFP d11/d11 |
olaparib_30nM |
1300 |
1.477121 |
968.75 |
1.3419355 |
1.695022 |
0.7916919 |
| d11/d11+GFP-ALC1 |
exp2 |
d11/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_30nM |
820 |
1.477121 |
620.00 |
1.3225806 |
1.418106 |
0.9326389 |
| h/d11+GFP |
exp3 |
h/d11 |
GFP |
exp3 GFP h/d11 |
GFP h/d11 |
olaparib_30nM |
2184 |
1.477121 |
1818.50 |
1.2009898 |
1.267157 |
0.9477832 |
| h/d11+GFP-ALC1 |
exp3 |
h/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_30nM |
2066 |
1.477121 |
1884.00 |
1.0966030 |
1.168804 |
0.9382264 |
| d11/d11+GFP |
exp3 |
d11/d11 |
GFP |
exp3 GFP d11/d11 |
GFP d11/d11 |
olaparib_30nM |
680 |
1.477121 |
535.00 |
1.2710280 |
1.695022 |
0.7498592 |
| d11/d11+GFP-ALC1 |
exp3 |
d11/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_30nM |
356 |
1.477121 |
295.00 |
1.2067797 |
1.418106 |
0.8509800 |
| h/d11+GFP |
exp4 |
h/d11 |
GFP |
exp4 GFP h/d11 |
GFP h/d11 |
olaparib_30nM |
2052 |
1.477121 |
1720.00 |
1.1930233 |
1.267157 |
0.9414962 |
| h/d11+GFP-ALC1 |
exp4 |
h/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_30nM |
2120 |
1.477121 |
1886.00 |
1.1240721 |
1.168804 |
0.9617283 |
| d11/d11+GFP |
exp4 |
d11/d11 |
GFP |
exp4 GFP d11/d11 |
GFP d11/d11 |
olaparib_30nM |
632 |
1.477121 |
485.00 |
1.3030928 |
1.695022 |
0.7687762 |
| d11/d11+GFP-ALC1 |
exp4 |
d11/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_30nM |
482 |
1.477121 |
376.25 |
1.2810631 |
1.418106 |
0.9033622 |
| h/d11+GFP |
exp1 |
h/d11 |
GFP |
exp1 GFP h/d11 |
GFP h/d11 |
olaparib_300nM |
1956 |
2.477121 |
2364.00 |
0.8274112 |
1.267157 |
0.6529667 |
| h/d11+GFP-ALC1 |
exp1 |
h/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_300nM |
2150 |
2.477121 |
2270.00 |
0.9471366 |
1.168804 |
0.8103466 |
| d11/d11+GFP |
exp1 |
d11/d11 |
GFP |
exp1 GFP d11/d11 |
GFP d11/d11 |
olaparib_300nM |
660 |
2.477121 |
910.25 |
0.7250755 |
1.695022 |
0.4277675 |
| d11/d11+GFP-ALC1 |
exp1 |
d11/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_300nM |
550 |
2.477121 |
700.00 |
0.7857143 |
1.418106 |
0.5540590 |
| h/d11+GFP |
exp2 |
h/d11 |
GFP |
exp2 GFP h/d11 |
GFP h/d11 |
olaparib_300nM |
1675 |
2.477121 |
2086.25 |
0.8028760 |
1.267157 |
0.6336043 |
| h/d11+GFP-ALC1 |
exp2 |
h/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_300nM |
1980 |
2.477121 |
2097.50 |
0.9439809 |
1.168804 |
0.8076467 |
| d11/d11+GFP |
exp2 |
d11/d11 |
GFP |
exp2 GFP d11/d11 |
GFP d11/d11 |
olaparib_300nM |
740 |
2.477121 |
968.75 |
0.7638710 |
1.695022 |
0.4506554 |
| d11/d11+GFP-ALC1 |
exp2 |
d11/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_300nM |
520 |
2.477121 |
620.00 |
0.8387097 |
1.418106 |
0.5914295 |
| h/d11+GFP |
exp3 |
h/d11 |
GFP |
exp3 GFP h/d11 |
GFP h/d11 |
olaparib_300nM |
1586 |
2.477121 |
1818.50 |
0.8721474 |
1.267157 |
0.6882711 |
| h/d11+GFP-ALC1 |
exp3 |
h/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_300nM |
1760 |
2.477121 |
1884.00 |
0.9341826 |
1.168804 |
0.7992635 |
| d11/d11+GFP |
exp3 |
d11/d11 |
GFP |
exp3 GFP d11/d11 |
GFP d11/d11 |
olaparib_300nM |
440 |
2.477121 |
535.00 |
0.8224299 |
1.695022 |
0.4852030 |
| d11/d11+GFP-ALC1 |
exp3 |
d11/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_300nM |
288 |
2.477121 |
295.00 |
0.9762712 |
1.418106 |
0.6884332 |
| h/d11+GFP |
exp4 |
h/d11 |
GFP |
exp4 GFP h/d11 |
GFP h/d11 |
olaparib_300nM |
1552 |
2.477121 |
1720.00 |
0.9023256 |
1.267157 |
0.7120868 |
| h/d11+GFP-ALC1 |
exp4 |
h/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_300nM |
1850 |
2.477121 |
1886.00 |
0.9809120 |
1.168804 |
0.8392440 |
| d11/d11+GFP |
exp4 |
d11/d11 |
GFP |
exp4 GFP d11/d11 |
GFP d11/d11 |
olaparib_300nM |
340 |
2.477121 |
485.00 |
0.7010309 |
1.695022 |
0.4135821 |
| d11/d11+GFP-ALC1 |
exp4 |
d11/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_300nM |
284 |
2.477121 |
376.25 |
0.7548173 |
1.418106 |
0.5322715 |
| h/d11+GFP |
exp1 |
h/d11 |
GFP |
exp1 GFP h/d11 |
GFP h/d11 |
olaparib_3000nM |
1980 |
3.477121 |
2364.00 |
0.8375635 |
1.267157 |
0.6609786 |
| h/d11+GFP-ALC1 |
exp1 |
h/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_3000nM |
2010 |
3.477121 |
2270.00 |
0.8854626 |
1.168804 |
0.7575799 |
| d11/d11+GFP |
exp1 |
d11/d11 |
GFP |
exp1 GFP d11/d11 |
GFP d11/d11 |
olaparib_3000nM |
109 |
3.477121 |
910.25 |
0.1197473 |
1.695022 |
0.0706465 |
| d11/d11+GFP-ALC1 |
exp1 |
d11/d11 |
GFP-ALC1 |
exp1 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_3000nM |
360 |
3.477121 |
700.00 |
0.5142857 |
1.418106 |
0.3626568 |
| h/d11+GFP |
exp2 |
h/d11 |
GFP |
exp2 GFP h/d11 |
GFP h/d11 |
olaparib_3000nM |
1480 |
3.477121 |
2086.25 |
0.7094068 |
1.267157 |
0.5598414 |
| h/d11+GFP-ALC1 |
exp2 |
h/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_3000nM |
1840 |
3.477121 |
2097.50 |
0.8772348 |
1.168804 |
0.7505404 |
| d11/d11+GFP |
exp2 |
d11/d11 |
GFP |
exp2 GFP d11/d11 |
GFP d11/d11 |
olaparib_3000nM |
95 |
3.477121 |
968.75 |
0.0980645 |
1.695022 |
0.0578544 |
| d11/d11+GFP-ALC1 |
exp2 |
d11/d11 |
GFP-ALC1 |
exp2 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_3000nM |
220 |
3.477121 |
620.00 |
0.3548387 |
1.418106 |
0.2502202 |
| h/d11+GFP |
exp3 |
h/d11 |
GFP |
exp3 GFP h/d11 |
GFP h/d11 |
olaparib_3000nM |
1148 |
3.477121 |
1818.50 |
0.6312895 |
1.267157 |
0.4981937 |
| h/d11+GFP-ALC1 |
exp3 |
h/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_3000nM |
1518 |
3.477121 |
1884.00 |
0.8057325 |
1.168804 |
0.6893648 |
| d11/d11+GFP |
exp3 |
d11/d11 |
GFP |
exp3 GFP d11/d11 |
GFP d11/d11 |
olaparib_3000nM |
180 |
3.477121 |
535.00 |
0.3364486 |
1.695022 |
0.1984921 |
| d11/d11+GFP-ALC1 |
exp3 |
d11/d11 |
GFP-ALC1 |
exp3 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_3000nM |
140 |
3.477121 |
295.00 |
0.4745763 |
1.418106 |
0.3346550 |
| h/d11+GFP |
exp4 |
h/d11 |
GFP |
exp4 GFP h/d11 |
GFP h/d11 |
olaparib_3000nM |
1020 |
3.477121 |
1720.00 |
0.5930233 |
1.267157 |
0.4679952 |
| h/d11+GFP-ALC1 |
exp4 |
h/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 h/d11 |
GFP-ALC1 h/d11 |
olaparib_3000nM |
1314 |
3.477121 |
1886.00 |
0.6967126 |
1.168804 |
0.5960901 |
| d11/d11+GFP |
exp4 |
d11/d11 |
GFP |
exp4 GFP d11/d11 |
GFP d11/d11 |
olaparib_3000nM |
152 |
3.477121 |
485.00 |
0.3134021 |
1.695022 |
0.1848955 |
| d11/d11+GFP-ALC1 |
exp4 |
d11/d11 |
GFP-ALC1 |
exp4 GFP-ALC1 d11/d11 |
GFP-ALC1 d11/d11 |
olaparib_3000nM |
211 |
3.477121 |
376.25 |
0.5607973 |
1.418106 |
0.3954552 |
Plot Data
library(ggplot2)
# raw data
ggplot(dataset, aes(x=Olaparib, y=Counts)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=BRCA)) +
geom_point(aes(colour=BRCA, shape=Experiment), size=2) +
facet_grid(. ~ Alc1) +
xlab(label = "Olaparib (log10 nM)") +
scale_shape_manual(values=15:20) +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
facet_grid(. ~ Alc1) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Linear
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
facet_grid(. ~ Alc1) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
facet_grid(. ~ Alc1) +
xlab(label = "Olaparib (log10 nM)")+
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Quadratic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
facet_grid(. ~ Alc1) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
facet_grid(. ~ Alc1) +
xlab(label = "Olaparib (log10 nM)")+
scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Cubic
ggplot(dataset, aes(x=Olaparib, y=NormCounts2, color=BRCA)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_point(aes(colour=BRCA), size=2) +
geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
facet_grid(. ~ Alc1) +
xlab(label = "Olaparib (log10 nM)") +
scale_color_manual(values=c("#000000","#FF0000"))

library(Cairo)
cairo_pdf("Figure5H.pdf", width = 5, height = 4, family = "Arial")
ggplot(dataset, aes(x=Olaparib, y=NormCounts2)) +
theme_bw() +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
axis.line = element_line(colour = "black"), text = element_text(size=14),
panel.border = element_blank(), panel.background = element_blank()) +
geom_point(aes(colour = BRCA, shape = Alc1), size=1.75) +
geom_smooth(method=lm, formula = y ~ poly(x,3), se=TRUE,
aes(group = GSID,colour = BRCA, linetype = Alc1), fill='#DDDDDD', size=0.5) +
xlab(label = "Olaparib (log10 nM)") +
ylab(label = "Normalized Counts") +
scale_color_manual(values=c("#000000","#FF0000")) +
guides(linetype = guide_legend(override.aes= list(color = "#555555")))
dev.off()
## quartz_off_screen
## 2
Models
library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)
Linear formula
fit1 <- lm(Counts ~ Experiment + Olaparib*BRCA*Alc1, data = dataset)
print(summary(fit1))
##
## Call:
## lm(formula = Counts ~ Experiment + Olaparib * BRCA * Alc1, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -341.65 -99.09 -8.29 101.69 323.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2880.21 83.47 34.504 < 2e-16 ***
## Experimentexp2 -117.94 60.23 -1.958 0.05550 .
## Experimentexp3 -427.94 60.23 -7.105 3.06e-09 ***
## Experimentexp4 -444.25 60.23 -7.375 1.12e-09 ***
## Olaparib -342.06 33.15 -10.318 2.78e-14 ***
## BRCAd11/d11 -1309.45 105.90 -12.365 < 2e-16 ***
## Alc1GFP-ALC1 -223.76 105.90 -2.113 0.03934 *
## Olaparib:BRCAd11/d11 19.92 46.88 0.425 0.67258
## Olaparib:Alc1GFP-ALC1 140.46 46.88 2.996 0.00416 **
## BRCAd11/d11:Alc1GFP-ALC1 -338.26 149.77 -2.259 0.02805 *
## Olaparib:BRCAd11/d11:Alc1GFP-ALC1 39.90 66.30 0.602 0.54988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 170.4 on 53 degrees of freedom
## Multiple R-squared: 0.9645, Adjusted R-squared: 0.9578
## F-statistic: 144 on 10 and 53 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit1))
## AIC: 851.2118
simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ Olaparib*BRCA*Alc1, data = dataset)
print(summary(fit2))
##
## Call:
## lm(formula = NormCounts ~ Olaparib * BRCA * Alc1, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.229003 -0.068268 -0.007592 0.059351 0.260029
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.32580 0.04911 26.998 < 2e-16 ***
## Olaparib -0.17537 0.02174 -8.066 6.02e-11 ***
## BRCAd11/d11 0.47329 0.06945 6.815 6.99e-09 ***
## Alc1GFP-ALC1 -0.13861 0.06945 -1.996 0.0508 .
## Olaparib:BRCAd11/d11 -0.25475 0.03075 -8.286 2.62e-11 ***
## Olaparib:Alc1GFP-ALC1 0.07461 0.03075 2.427 0.0185 *
## BRCAd11/d11:Alc1GFP-ALC1 -0.14652 0.09822 -1.492 0.1414
## Olaparib:BRCAd11/d11:Alc1GFP-ALC1 0.07886 0.04348 1.814 0.0751 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1117 on 56 degrees of freedom
## Multiple R-squared: 0.9195, Adjusted R-squared: 0.9095
## F-statistic: 91.41 on 7 and 56 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit2))
## AIC: -89.46234
simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ Olaparib*BRCA*Alc1, data = dataset)
print(summary(fit3))
##
## Call:
## lm(formula = NormCounts2 ~ Olaparib * BRCA * Alc1, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.139051 -0.050234 -0.005139 0.049554 0.153408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.046283 0.033805 30.950 < 2e-16 ***
## Olaparib -0.138394 0.014966 -9.247 7.21e-13 ***
## BRCAd11/d11 0.015117 0.047808 0.316 0.7530
## Alc1GFP-ALC1 -0.030549 0.047808 -0.639 0.5254
## Olaparib:BRCAd11/d11 -0.115361 0.021165 -5.450 1.17e-06 ***
## Olaparib:Alc1GFP-ALC1 0.052187 0.021165 2.466 0.0168 *
## BRCAd11/d11:Alc1GFP-ALC1 0.036750 0.067611 0.544 0.5889
## Olaparib:BRCAd11/d11:Alc1GFP-ALC1 0.006484 0.029932 0.217 0.8293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07691 on 56 degrees of freedom
## Multiple R-squared: 0.9242, Adjusted R-squared: 0.9147
## F-statistic: 97.52 on 7 and 56 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit3))
## AIC: -137.2569
simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ Olaparib*BRCA*Alc1 + (1|UID), data = dataset)
print(summary(fit4))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ Olaparib * BRCA * Alc1 + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 783.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.85140 -0.50837 0.00144 0.46463 2.11990
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 47106 217.0
## Residual 29754 172.5
## Number of obs: 64, groups: UID, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2632.68 132.38 19.39 19.887 2.31e-14
## Olaparib -342.06 33.57 44.00 -10.191 3.72e-13
## BRCAd11/d11 -1309.45 187.22 19.39 -6.994 1.04e-06
## Alc1GFP-ALC1 -223.76 187.22 19.39 -1.195 0.24643
## Olaparib:BRCAd11/d11 19.92 47.47 44.00 0.420 0.67673
## Olaparib:Alc1GFP-ALC1 140.46 47.47 44.00 2.959 0.00496
## BRCAd11/d11:Alc1GFP-ALC1 -338.26 264.76 19.39 -1.278 0.21648
## Olaparib:BRCAd11/d11:Alc1GFP-ALC1 39.90 67.13 44.00 0.594 0.55531
##
## (Intercept) ***
## Olaparib ***
## BRCAd11/d11 ***
## Alc1GFP-ALC1
## Olaparib:BRCAd11/d11
## Olaparib:Alc1GFP-ALC1 **
## BRCAd11/d11:Alc1GFP-ALC1
## Olaparib:BRCAd11/d11:Alc1GFP-ALC1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Olaprb BRCAd11/11 A1GFP- Ol:BRCA11/11 O:A1GF BRCA11/11:
## Olaparib -0.471
## BRCAd11/d11 -0.707 0.333
## Al1GFP-ALC1 -0.707 0.333 0.500
## Ol:BRCA11/11 0.333 -0.707 -0.471 -0.236
## O:A1GFP-ALC 0.333 -0.707 -0.236 -0.471 0.500
## BRCA11/11:A 0.500 -0.236 -0.707 -0.707 0.333 0.333
## O:BRCA11/11: -0.236 0.500 0.333 0.333 -0.707 -0.707 -0.471
cat("AIC: ", AIC(fit4))
## AIC: 803.8541
simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula
fit5 <- lm(Counts ~ Experiment + poly(Olaparib,2)*BRCA*Alc1, data = dataset)
print(summary(fit5))
##
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 2) * BRCA *
## Alc1, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -309.91 -101.30 1.91 81.79 383.55
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 2244.72 53.04 42.324
## Experimentexp2 -117.94 56.70 -2.080
## Experimentexp3 -427.94 56.70 -7.548
## Experimentexp4 -444.25 56.70 -7.835
## poly(Olaparib, 2)1 -3515.62 320.74 -10.961
## poly(Olaparib, 2)2 -707.28 320.74 -2.205
## BRCAd11/d11 -1272.44 56.70 -22.442
## Alc1GFP-ALC1 37.19 56.70 0.656
## poly(Olaparib, 2)1:BRCAd11/d11 204.78 453.59 0.451
## poly(Olaparib, 2)2:BRCAd11/d11 75.06 453.59 0.165
## poly(Olaparib, 2)1:Alc1GFP-ALC1 1443.58 453.59 3.183
## poly(Olaparib, 2)2:Alc1GFP-ALC1 418.64 453.59 0.923
## BRCAd11/d11:Alc1GFP-ALC1 -264.13 80.18 -3.294
## poly(Olaparib, 2)1:BRCAd11/d11:Alc1GFP-ALC1 410.10 641.47 0.639
## poly(Olaparib, 2)2:BRCAd11/d11:Alc1GFP-ALC1 -145.95 641.47 -0.228
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Experimentexp2 0.04277 *
## Experimentexp3 9.45e-10 ***
## Experimentexp4 3.41e-10 ***
## poly(Olaparib, 2)1 8.77e-15 ***
## poly(Olaparib, 2)2 0.03216 *
## BRCAd11/d11 < 2e-16 ***
## Alc1GFP-ALC1 0.51497
## poly(Olaparib, 2)1:BRCAd11/d11 0.65365
## poly(Olaparib, 2)2:BRCAd11/d11 0.86924
## poly(Olaparib, 2)1:Alc1GFP-ALC1 0.00254 **
## poly(Olaparib, 2)2:Alc1GFP-ALC1 0.36056
## BRCAd11/d11:Alc1GFP-ALC1 0.00184 **
## poly(Olaparib, 2)1:BRCAd11/d11:Alc1GFP-ALC1 0.52560
## poly(Olaparib, 2)2:BRCAd11/d11:Alc1GFP-ALC1 0.82096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 160.4 on 49 degrees of freedom
## Multiple R-squared: 0.9709, Adjusted R-squared: 0.9626
## F-statistic: 116.9 on 14 and 49 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit5))
## AIC: 846.4481
simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(Olaparib,2)*BRCA*Alc1, data = dataset)
print(summary(fit6))
##
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 2) * BRCA * Alc1, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.146645 -0.052636 -0.003375 0.058559 0.172530
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.000e+00 2.070e-02 48.321
## poly(Olaparib, 2)1 -1.802e+00 1.656e-01 -10.887
## poly(Olaparib, 2)2 -3.822e-01 1.656e-01 -2.308
## BRCAd11/d11 9.341e-17 2.927e-02 0.000
## Alc1GFP-ALC1 1.297e-16 2.927e-02 0.000
## poly(Olaparib, 2)1:BRCAd11/d11 -2.618e+00 2.341e-01 -11.183
## poly(Olaparib, 2)2:BRCAd11/d11 -4.290e-01 2.341e-01 -1.832
## poly(Olaparib, 2)1:Alc1GFP-ALC1 7.668e-01 2.341e-01 3.275
## poly(Olaparib, 2)2:Alc1GFP-ALC1 2.234e-01 2.341e-01 0.954
## BRCAd11/d11:Alc1GFP-ALC1 -1.608e-16 4.139e-02 0.000
## poly(Olaparib, 2)1:BRCAd11/d11:Alc1GFP-ALC1 8.105e-01 3.311e-01 2.448
## poly(Olaparib, 2)2:BRCAd11/d11:Alc1GFP-ALC1 -1.481e-01 3.311e-01 -0.447
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 2)1 5.1e-15 ***
## poly(Olaparib, 2)2 0.02499 *
## BRCAd11/d11 1.00000
## Alc1GFP-ALC1 1.00000
## poly(Olaparib, 2)1:BRCAd11/d11 1.9e-15 ***
## poly(Olaparib, 2)2:BRCAd11/d11 0.07265 .
## poly(Olaparib, 2)1:Alc1GFP-ALC1 0.00188 **
## poly(Olaparib, 2)2:Alc1GFP-ALC1 0.34433
## BRCAd11/d11:Alc1GFP-ALC1 1.00000
## poly(Olaparib, 2)1:BRCAd11/d11:Alc1GFP-ALC1 0.01778 *
## poly(Olaparib, 2)2:BRCAd11/d11:Alc1GFP-ALC1 0.65643
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08278 on 52 degrees of freedom
## Multiple R-squared: 0.959, Adjusted R-squared: 0.9503
## F-statistic: 110.5 on 11 and 52 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit6))
## AIC: -124.5855
simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(Olaparib,2)*BRCA*Alc1, data = dataset)
print(summary(fit7))
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 2) * BRCA * Alc1, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.103409 -0.043894 -0.002888 0.038995 0.136155
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.78917 0.01468 53.753
## poly(Olaparib, 2)1 -1.42239 0.11745 -12.110
## poly(Olaparib, 2)2 -0.30160 0.11745 -2.568
## BRCAd11/d11 -0.19921 0.02076 -9.594
## Alc1GFP-ALC1 0.06641 0.02076 3.198
## poly(Olaparib, 2)1:BRCAd11/d11 -1.18566 0.16610 -7.138
## poly(Olaparib, 2)2:BRCAd11/d11 -0.17696 0.16610 -1.065
## poly(Olaparib, 2)1:Alc1GFP-ALC1 0.53637 0.16610 3.229
## poly(Olaparib, 2)2:Alc1GFP-ALC1 0.16580 0.16610 0.998
## BRCAd11/d11:Alc1GFP-ALC1 0.04880 0.02936 1.662
## poly(Olaparib, 2)1:BRCAd11/d11:Alc1GFP-ALC1 0.06664 0.23490 0.284
## poly(Olaparib, 2)2:BRCAd11/d11:Alc1GFP-ALC1 -0.20615 0.23490 -0.878
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 2)1 < 2e-16 ***
## poly(Olaparib, 2)2 0.01315 *
## BRCAd11/d11 4.26e-13 ***
## Alc1GFP-ALC1 0.00235 **
## poly(Olaparib, 2)1:BRCAd11/d11 2.98e-09 ***
## poly(Olaparib, 2)2:BRCAd11/d11 0.29164
## poly(Olaparib, 2)1:Alc1GFP-ALC1 0.00215 **
## poly(Olaparib, 2)2:Alc1GFP-ALC1 0.32282
## BRCAd11/d11:Alc1GFP-ALC1 0.10256
## poly(Olaparib, 2)1:BRCAd11/d11:Alc1GFP-ALC1 0.77776
## poly(Olaparib, 2)2:BRCAd11/d11:Alc1GFP-ALC1 0.38421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05873 on 52 degrees of freedom
## Multiple R-squared: 0.959, Adjusted R-squared: 0.9503
## F-statistic: 110.4 on 11 and 52 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit7))
## AIC: -168.5295
simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(Olaparib,2)*BRCA*Alc1 + (1|UID), data = dataset)
print(summary(fit8))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 2) * BRCA * Alc1 + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 701.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6123 -0.4767 -0.1080 0.6053 1.9160
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 48100 219.3
## Residual 25776 160.5
## Number of obs: 64, groups: UID, 16
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1997.19 116.77 12.00
## poly(Olaparib, 2)1 -3515.62 321.10 40.00
## poly(Olaparib, 2)2 -707.28 321.10 40.00
## BRCAd11/d11 -1272.44 165.14 12.00
## Alc1GFP-ALC1 37.19 165.14 12.00
## poly(Olaparib, 2)1:BRCAd11/d11 204.78 454.10 40.00
## poly(Olaparib, 2)2:BRCAd11/d11 75.06 454.10 40.00
## poly(Olaparib, 2)1:Alc1GFP-ALC1 1443.58 454.10 40.00
## poly(Olaparib, 2)2:Alc1GFP-ALC1 418.64 454.10 40.00
## BRCAd11/d11:Alc1GFP-ALC1 -264.13 233.55 12.00
## poly(Olaparib, 2)1:BRCAd11/d11:Alc1GFP-ALC1 410.10 642.19 40.00
## poly(Olaparib, 2)2:BRCAd11/d11:Alc1GFP-ALC1 -145.95 642.19 40.00
## t value Pr(>|t|)
## (Intercept) 17.103 8.59e-10 ***
## poly(Olaparib, 2)1 -10.949 1.33e-13 ***
## poly(Olaparib, 2)2 -2.203 0.03344 *
## BRCAd11/d11 -7.705 5.51e-06 ***
## Alc1GFP-ALC1 0.225 0.82563
## poly(Olaparib, 2)1:BRCAd11/d11 0.451 0.65446
## poly(Olaparib, 2)2:BRCAd11/d11 0.165 0.86954
## poly(Olaparib, 2)1:Alc1GFP-ALC1 3.179 0.00285 **
## poly(Olaparib, 2)2:Alc1GFP-ALC1 0.922 0.36210
## BRCAd11/d11:Alc1GFP-ALC1 -1.131 0.28018
## poly(Olaparib, 2)1:BRCAd11/d11:Alc1GFP-ALC1 0.639 0.52673
## poly(Olaparib, 2)2:BRCAd11/d11:Alc1GFP-ALC1 -0.227 0.82137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) pl(O,2)1 pl(O,2)2 BRCAd11/11 A1GFP-
## ply(Olp,2)1 0.000
## ply(Olp,2)2 0.000 0.000
## BRCAd11/d11 -0.707 0.000 0.000
## Al1GFP-ALC1 -0.707 0.000 0.000 0.500
## pl(O,2)1:BRCA11/11 0.000 -0.707 0.000 0.000 0.000
## pl(O,2)2:BRCA11/11 0.000 0.000 -0.707 0.000 0.000
## p(O,2)1:A1G 0.000 -0.707 0.000 0.000 0.000
## p(O,2)2:A1G 0.000 0.000 -0.707 0.000 0.000
## BRCA11/11:A 0.500 0.000 0.000 -0.707 -0.707
## p(O,2)1:BRCA11/11: 0.000 0.500 0.000 0.000 0.000
## p(O,2)2:BRCA11/11: 0.000 0.000 0.500 0.000 0.000
## pl(O,2)1:BRCA11/11 pl(O,2)2:BRCA11/11 p(O,2)1:A p(O,2)2:A
## ply(Olp,2)1
## ply(Olp,2)2
## BRCAd11/d11
## Al1GFP-ALC1
## pl(O,2)1:BRCA11/11
## pl(O,2)2:BRCA11/11 0.000
## p(O,2)1:A1G 0.500 0.000
## p(O,2)2:A1G 0.000 0.500 0.000
## BRCA11/11:A 0.000 0.000 0.000 0.000
## p(O,2)1:BRCA11/11: -0.707 0.000 -0.707 0.000
## p(O,2)2:BRCA11/11: 0.000 -0.707 0.000 -0.707
## BRCA11/11: p(O,2)1:BRCA11/11:
## ply(Olp,2)1
## ply(Olp,2)2
## BRCAd11/d11
## Al1GFP-ALC1
## pl(O,2)1:BRCA11/11
## pl(O,2)2:BRCA11/11
## p(O,2)1:A1G
## p(O,2)2:A1G
## BRCA11/11:A
## p(O,2)1:BRCA11/11: 0.000
## p(O,2)2:BRCA11/11: 0.000 0.000
cat("AIC: ", AIC(fit8))
## AIC: 729.3739
simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula
fit9 <- lm(Counts ~ Experiment + poly(Olaparib,3)*BRCA*Alc1, data = dataset)
print(summary(fit9))
##
## Call:
## lm(formula = Counts ~ Experiment + poly(Olaparib, 3) * BRCA *
## Alc1, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -272.53 -100.58 8.31 74.47 367.41
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 2244.72 50.24 44.678
## Experimentexp2 -117.94 53.71 -2.196
## Experimentexp3 -427.94 53.71 -7.967
## Experimentexp4 -444.25 53.71 -8.271
## poly(Olaparib, 3)1 -3515.62 303.84 -11.571
## poly(Olaparib, 3)2 -707.28 303.84 -2.328
## poly(Olaparib, 3)3 891.45 303.84 2.934
## BRCAd11/d11 -1272.44 53.71 -23.690
## Alc1GFP-ALC1 37.19 53.71 0.692
## poly(Olaparib, 3)1:BRCAd11/d11 204.78 429.69 0.477
## poly(Olaparib, 3)2:BRCAd11/d11 75.06 429.69 0.175
## poly(Olaparib, 3)3:BRCAd11/d11 -657.51 429.69 -1.530
## poly(Olaparib, 3)1:Alc1GFP-ALC1 1443.58 429.69 3.360
## poly(Olaparib, 3)2:Alc1GFP-ALC1 418.64 429.69 0.974
## poly(Olaparib, 3)3:Alc1GFP-ALC1 -874.63 429.69 -2.036
## BRCAd11/d11:Alc1GFP-ALC1 -264.13 75.96 -3.477
## poly(Olaparib, 3)1:BRCAd11/d11:Alc1GFP-ALC1 410.10 607.67 0.675
## poly(Olaparib, 3)2:BRCAd11/d11:Alc1GFP-ALC1 -145.95 607.67 -0.240
## poly(Olaparib, 3)3:BRCAd11/d11:Alc1GFP-ALC1 832.44 607.67 1.370
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## Experimentexp2 0.03330 *
## Experimentexp3 3.85e-10 ***
## Experimentexp4 1.39e-10 ***
## poly(Olaparib, 3)1 4.44e-15 ***
## poly(Olaparib, 3)2 0.02448 *
## poly(Olaparib, 3)3 0.00525 **
## BRCAd11/d11 < 2e-16 ***
## Alc1GFP-ALC1 0.49227
## poly(Olaparib, 3)1:BRCAd11/d11 0.63598
## poly(Olaparib, 3)2:BRCAd11/d11 0.86210
## poly(Olaparib, 3)3:BRCAd11/d11 0.13297
## poly(Olaparib, 3)1:Alc1GFP-ALC1 0.00160 **
## poly(Olaparib, 3)2:Alc1GFP-ALC1 0.33512
## poly(Olaparib, 3)3:Alc1GFP-ALC1 0.04771 *
## BRCAd11/d11:Alc1GFP-ALC1 0.00113 **
## poly(Olaparib, 3)1:BRCAd11/d11:Alc1GFP-ALC1 0.50321
## poly(Olaparib, 3)2:BRCAd11/d11:Alc1GFP-ALC1 0.81128
## poly(Olaparib, 3)3:BRCAd11/d11:Alc1GFP-ALC1 0.17752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 151.9 on 45 degrees of freedom
## Multiple R-squared: 0.976, Adjusted R-squared: 0.9665
## F-statistic: 101.8 on 18 and 45 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit9))
## AIC: 842.0695
simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(Olaparib,3)*BRCA*Alc1, data = dataset)
print(summary(fit10))
##
## Call:
## lm(formula = NormCounts ~ poly(Olaparib, 3) * BRCA * Alc1, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.124929 -0.049253 -0.001148 0.039382 0.144743
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.000e+00 1.819e-02 54.989
## poly(Olaparib, 3)1 -1.802e+00 1.455e-01 -12.389
## poly(Olaparib, 3)2 -3.822e-01 1.455e-01 -2.627
## poly(Olaparib, 3)3 4.265e-01 1.455e-01 2.931
## BRCAd11/d11 -2.071e-17 2.572e-02 0.000
## Alc1GFP-ALC1 4.850e-17 2.572e-02 0.000
## poly(Olaparib, 3)1:BRCAd11/d11 -2.618e+00 2.057e-01 -12.726
## poly(Olaparib, 3)2:BRCAd11/d11 -4.290e-01 2.057e-01 -2.085
## poly(Olaparib, 3)3:BRCAd11/d11 -9.427e-02 2.057e-01 -0.458
## poly(Olaparib, 3)1:Alc1GFP-ALC1 7.668e-01 2.057e-01 3.727
## poly(Olaparib, 3)2:Alc1GFP-ALC1 2.234e-01 2.057e-01 1.086
## poly(Olaparib, 3)3:Alc1GFP-ALC1 -4.169e-01 2.057e-01 -2.026
## BRCAd11/d11:Alc1GFP-ALC1 -8.866e-17 3.637e-02 0.000
## poly(Olaparib, 3)1:BRCAd11/d11:Alc1GFP-ALC1 8.105e-01 2.910e-01 2.786
## poly(Olaparib, 3)2:BRCAd11/d11:Alc1GFP-ALC1 -1.481e-01 2.910e-01 -0.509
## poly(Olaparib, 3)3:BRCAd11/d11:Alc1GFP-ALC1 4.268e-01 2.910e-01 1.467
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 3)1 < 2e-16 ***
## poly(Olaparib, 3)2 0.011533 *
## poly(Olaparib, 3)3 0.005156 **
## BRCAd11/d11 1.000000
## Alc1GFP-ALC1 1.000000
## poly(Olaparib, 3)1:BRCAd11/d11 < 2e-16 ***
## poly(Olaparib, 3)2:BRCAd11/d11 0.042409 *
## poly(Olaparib, 3)3:BRCAd11/d11 0.648902
## poly(Olaparib, 3)1:Alc1GFP-ALC1 0.000511 ***
## poly(Olaparib, 3)2:Alc1GFP-ALC1 0.282887
## poly(Olaparib, 3)3:Alc1GFP-ALC1 0.048309 *
## BRCAd11/d11:Alc1GFP-ALC1 1.000000
## poly(Olaparib, 3)1:BRCAd11/d11:Alc1GFP-ALC1 0.007625 **
## poly(Olaparib, 3)2:BRCAd11/d11:Alc1GFP-ALC1 0.612977
## poly(Olaparib, 3)3:BRCAd11/d11:Alc1GFP-ALC1 0.148907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07274 on 48 degrees of freedom
## Multiple R-squared: 0.9708, Adjusted R-squared: 0.9616
## F-statistic: 106.2 on 15 and 48 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit10))
## AIC: -138.2543
simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA*Alc1, data = dataset)
print(summary(fit11))
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * BRCA * Alc1, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.102304 -0.035893 -0.000865 0.029825 0.114226
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.78917 0.01285 61.407
## poly(Olaparib, 3)1 -1.42239 0.10281 -13.835
## poly(Olaparib, 3)2 -0.30160 0.10281 -2.933
## poly(Olaparib, 3)3 0.33655 0.10281 3.273
## BRCAd11/d11 -0.19921 0.01817 -10.961
## Alc1GFP-ALC1 0.06641 0.01817 3.654
## poly(Olaparib, 3)1:BRCAd11/d11 -1.18566 0.14540 -8.155
## poly(Olaparib, 3)2:BRCAd11/d11 -0.17696 0.14540 -1.217
## poly(Olaparib, 3)3:BRCAd11/d11 -0.14057 0.14540 -0.967
## poly(Olaparib, 3)1:Alc1GFP-ALC1 0.53637 0.14540 3.689
## poly(Olaparib, 3)2:Alc1GFP-ALC1 0.16580 0.14540 1.140
## poly(Olaparib, 3)3:Alc1GFP-ALC1 -0.32837 0.14540 -2.258
## BRCAd11/d11:Alc1GFP-ALC1 0.04880 0.02570 1.898
## poly(Olaparib, 3)1:BRCAd11/d11:Alc1GFP-ALC1 0.06664 0.20562 0.324
## poly(Olaparib, 3)2:BRCAd11/d11:Alc1GFP-ALC1 -0.20615 0.20562 -1.003
## poly(Olaparib, 3)3:BRCAd11/d11:Alc1GFP-ALC1 0.37365 0.20562 1.817
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## poly(Olaparib, 3)1 < 2e-16 ***
## poly(Olaparib, 3)2 0.005126 **
## poly(Olaparib, 3)3 0.001974 **
## BRCAd11/d11 1.16e-14 ***
## Alc1GFP-ALC1 0.000639 ***
## poly(Olaparib, 3)1:BRCAd11/d11 1.29e-10 ***
## poly(Olaparib, 3)2:BRCAd11/d11 0.229537
## poly(Olaparib, 3)3:BRCAd11/d11 0.338503
## poly(Olaparib, 3)1:Alc1GFP-ALC1 0.000574 ***
## poly(Olaparib, 3)2:Alc1GFP-ALC1 0.259820
## poly(Olaparib, 3)3:Alc1GFP-ALC1 0.028500 *
## BRCAd11/d11:Alc1GFP-ALC1 0.063656 .
## poly(Olaparib, 3)1:BRCAd11/d11:Alc1GFP-ALC1 0.747272
## poly(Olaparib, 3)2:BRCAd11/d11:Alc1GFP-ALC1 0.321110
## poly(Olaparib, 3)3:BRCAd11/d11:Alc1GFP-ALC1 0.075440 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05141 on 48 degrees of freedom
## Multiple R-squared: 0.971, Adjusted R-squared: 0.9619
## F-statistic: 107 on 15 and 48 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit11))
## AIC: -182.6914
simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(Olaparib,3)*BRCA*Alc1 + (1|UID), data = dataset)
print(summary(fit12))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Olaparib, 3) * BRCA * Alc1 + (1 | UID)
## Data: dataset
##
## REML criterion at convergence: 638.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.71965 -0.44043 -0.03705 0.52519 1.85497
##
## Random effects:
## Groups Name Variance Std.Dev.
## UID (Intercept) 48923 221.2
## Residual 22484 149.9
## Number of obs: 64, groups: UID, 16
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1997.19 116.77 12.00
## poly(Olaparib, 3)1 -3515.62 299.89 36.00
## poly(Olaparib, 3)2 -707.28 299.89 36.00
## poly(Olaparib, 3)3 891.45 299.89 36.00
## BRCAd11/d11 -1272.44 165.14 12.00
## Alc1GFP-ALC1 37.19 165.14 12.00
## poly(Olaparib, 3)1:BRCAd11/d11 204.78 424.11 36.00
## poly(Olaparib, 3)2:BRCAd11/d11 75.06 424.11 36.00
## poly(Olaparib, 3)3:BRCAd11/d11 -657.51 424.11 36.00
## poly(Olaparib, 3)1:Alc1GFP-ALC1 1443.58 424.11 36.00
## poly(Olaparib, 3)2:Alc1GFP-ALC1 418.64 424.11 36.00
## poly(Olaparib, 3)3:Alc1GFP-ALC1 -874.63 424.11 36.00
## BRCAd11/d11:Alc1GFP-ALC1 -264.13 233.55 12.00
## poly(Olaparib, 3)1:BRCAd11/d11:Alc1GFP-ALC1 410.10 599.78 36.00
## poly(Olaparib, 3)2:BRCAd11/d11:Alc1GFP-ALC1 -145.95 599.78 36.00
## poly(Olaparib, 3)3:BRCAd11/d11:Alc1GFP-ALC1 832.44 599.78 36.00
## t value Pr(>|t|)
## (Intercept) 17.103 8.59e-10 ***
## poly(Olaparib, 3)1 -11.723 7.54e-14 ***
## poly(Olaparib, 3)2 -2.358 0.02390 *
## poly(Olaparib, 3)3 2.973 0.00524 **
## BRCAd11/d11 -7.705 5.51e-06 ***
## Alc1GFP-ALC1 0.225 0.82563
## poly(Olaparib, 3)1:BRCAd11/d11 0.483 0.63214
## poly(Olaparib, 3)2:BRCAd11/d11 0.177 0.86051
## poly(Olaparib, 3)3:BRCAd11/d11 -1.550 0.12981
## poly(Olaparib, 3)1:Alc1GFP-ALC1 3.404 0.00164 **
## poly(Olaparib, 3)2:Alc1GFP-ALC1 0.987 0.33018
## poly(Olaparib, 3)3:Alc1GFP-ALC1 -2.062 0.04646 *
## BRCAd11/d11:Alc1GFP-ALC1 -1.131 0.28018
## poly(Olaparib, 3)1:BRCAd11/d11:Alc1GFP-ALC1 0.684 0.49851
## poly(Olaparib, 3)2:BRCAd11/d11:Alc1GFP-ALC1 -0.243 0.80912
## poly(Olaparib, 3)3:BRCAd11/d11:Alc1GFP-ALC1 1.388 0.17370
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("AIC: ", AIC(fit12))
## AIC: 674.9294
simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Compare Results
ICtab(fit1,fit2,fit3,fit4,
fit5,fit6,fit7,fit8,
fit9,fit10,fit11,fit12,
base=T)
## AIC dAIC df
## fit11 -182.7 0.0 17
## fit7 -168.5 14.2 13
## fit10 -138.3 44.4 17
## fit3 -137.3 45.4 9
## fit6 -124.6 58.1 13
## fit2 -89.5 93.2 9
## fit12 674.9 857.6 18
## fit8 729.4 912.1 14
## fit4 803.9 986.5 10
## fit9 842.1 1024.8 20
## fit5 846.4 1029.1 16
## fit1 851.2 1033.9 12
Final Result
fit <- fit11
output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("Alc1", paste0(" ",levels(dataset$Alc1)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$Alc1)[1], sep = " in " )
rownames(output) <- gsub("BRCA", paste0(" ",levels(dataset$BRCA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$BRCA)[1], sep = " in " )
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$BRCA)[1], sep = " " )
# suggested result table
kable(output, row.names = T)
| Olaparib1 in GFP h/d11 |
-1.4223910 |
0.1028122 |
-13.8348519 |
0.0000000 |
| Olaparib2 in GFP h/d11 |
-0.3015955 |
0.1028122 |
-2.9334619 |
0.0051264 |
| Olaparib3 in GFP h/d11 |
0.3365522 |
0.1028122 |
3.2734670 |
0.0019739 |
| Olaparib1: h/d11 vs. d11/d11 in GFP |
-1.1856592 |
0.1453983 |
-8.1545579 |
0.0000000 |
| Olaparib2: h/d11 vs. d11/d11 in GFP |
-0.1769563 |
0.1453983 |
-1.2170450 |
0.2295372 |
| Olaparib3: h/d11 vs. d11/d11 in GFP |
-0.1405672 |
0.1453983 |
-0.9667731 |
0.3385035 |
| Olaparib1: GFP vs. GFP-ALC1 in h/d11 |
0.5363728 |
0.1453983 |
3.6889887 |
0.0005737 |
| Olaparib2: GFP vs. GFP-ALC1 in h/d11 |
0.1657976 |
0.1453983 |
1.1402990 |
0.2598197 |
| Olaparib3: GFP vs. GFP-ALC1 in h/d11 |
-0.3283737 |
0.1453983 |
-2.2584419 |
0.0285003 |
| Olaparib1: h/d11 vs. d11/d11: GFP vs. GFP-ALC1 |
0.0666424 |
0.2056243 |
0.3240977 |
0.7472722 |
| Olaparib2: h/d11 vs. d11/d11: GFP vs. GFP-ALC1 |
-0.2061467 |
0.2056243 |
-1.0025405 |
0.3211100 |
| Olaparib3: h/d11 vs. d11/d11: GFP vs. GFP-ALC1 |
0.3736504 |
0.2056243 |
1.8171508 |
0.0754397 |
write.table(output, file = "Figure5H_Stats_Ref_h_d11.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
dataset$BRCA <- relevel(dataset$BRCA, ref = "d11/d11")
fit <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA*Alc1, data = dataset)
output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("Alc1", paste0(" ",levels(dataset$Alc1)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$Alc1)[1], sep = " in " )
rownames(output) <- gsub("BRCA", paste0(" ",levels(dataset$BRCA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$BRCA)[1], sep = " in " )
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$BRCA)[1], sep = " " )
# suggested result table
kable(output, row.names = T)
| Olaparib1 in GFP d11/d11 |
-2.6080502 |
0.1028122 |
-25.3671384 |
0.0000000 |
| Olaparib2 in GFP d11/d11 |
-0.4785519 |
0.1028122 |
-4.6546235 |
0.0000259 |
| Olaparib3 in GFP d11/d11 |
0.1959850 |
0.1028122 |
1.9062433 |
0.0626155 |
| Olaparib1: d11/d11 vs. h/d11 in GFP |
1.1856592 |
0.1453983 |
8.1545579 |
0.0000000 |
| Olaparib2: d11/d11 vs. h/d11 in GFP |
0.1769563 |
0.1453983 |
1.2170450 |
0.2295372 |
| Olaparib3: d11/d11 vs. h/d11 in GFP |
0.1405672 |
0.1453983 |
0.9667731 |
0.3385035 |
| Olaparib1: GFP vs. GFP-ALC1 in d11/d11 |
0.6030152 |
0.1453983 |
4.1473320 |
0.0001363 |
| Olaparib2: GFP vs. GFP-ALC1 in d11/d11 |
-0.0403491 |
0.1453983 |
-0.2775073 |
0.7825824 |
| Olaparib3: GFP vs. GFP-ALC1 in d11/d11 |
0.0452767 |
0.1453983 |
0.3113974 |
0.7568470 |
| Olaparib1: d11/d11 vs. h/d11: GFP vs. GFP-ALC1 |
-0.0666424 |
0.2056243 |
-0.3240977 |
0.7472722 |
| Olaparib2: d11/d11 vs. h/d11: GFP vs. GFP-ALC1 |
0.2061467 |
0.2056243 |
1.0025405 |
0.3211100 |
| Olaparib3: d11/d11 vs. h/d11: GFP vs. GFP-ALC1 |
-0.3736504 |
0.2056243 |
-1.8171508 |
0.0754397 |
write.table(output, file = "Figure5H_Stats_Ref_d11_d11.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
# re-fit with GFP-ALC1 siBRCA1 reference
dataset$Alc1 <- relevel(dataset$Alc1, ref = "GFP-ALC1")
dataset$BRCA <- relevel(dataset$BRCA, ref = "d11/d11")
fit <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA*Alc1, data = dataset)
output <- coef(summary(fit))
output <- output[grep("Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("Alc1", paste0(" ",levels(dataset$Alc1)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$Alc1)[1], sep = " in " )
rownames(output) <- gsub("BRCA", paste0(" ",levels(dataset$BRCA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs|in", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs|in", rownames(output)))], levels(dataset$BRCA)[1], sep = " in " )
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$BRCA)[1], sep = " " )
# suggested result table
kable(output, row.names = T)
| Olaparib1 in GFP-ALC1 d11/d11 |
-2.0050350 |
0.1028122 |
-19.5019252 |
0.0000000 |
| Olaparib2 in GFP-ALC1 d11/d11 |
-0.5189010 |
0.1028122 |
-5.0470781 |
0.0000069 |
| Olaparib3 in GFP-ALC1 d11/d11 |
0.2412616 |
0.1028122 |
2.3466257 |
0.0231194 |
| Olaparib1: d11/d11 vs. h/d11 in GFP-ALC1 |
1.1190169 |
0.1453983 |
7.6962146 |
0.0000000 |
| Olaparib2: d11/d11 vs. h/d11 in GFP-ALC1 |
0.3831030 |
0.1453983 |
2.6348514 |
0.0112991 |
| Olaparib3: d11/d11 vs. h/d11 in GFP-ALC1 |
-0.2330832 |
0.1453983 |
-1.6030662 |
0.1154810 |
| Olaparib1: GFP-ALC1 vs. GFP in d11/d11 |
-0.6030152 |
0.1453983 |
-4.1473320 |
0.0001363 |
| Olaparib2: GFP-ALC1 vs. GFP in d11/d11 |
0.0403491 |
0.1453983 |
0.2775073 |
0.7825824 |
| Olaparib3: GFP-ALC1 vs. GFP in d11/d11 |
-0.0452767 |
0.1453983 |
-0.3113974 |
0.7568470 |
| Olaparib1: d11/d11 vs. h/d11: GFP-ALC1 vs. GFP |
0.0666424 |
0.2056243 |
0.3240977 |
0.7472722 |
| Olaparib2: d11/d11 vs. h/d11: GFP-ALC1 vs. GFP |
-0.2061467 |
0.2056243 |
-1.0025405 |
0.3211100 |
| Olaparib3: d11/d11 vs. h/d11: GFP-ALC1 vs. GFP |
0.3736504 |
0.2056243 |
1.8171508 |
0.0754397 |
write.table(output, file = "Figure5H_Stats_Ref_GFP-ALC1_d11_d11.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
Anova
fit11a <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA*Alc1, data = dataset)
fit11b <- lm(NormCounts2 ~ poly(Olaparib,3)*BRCA+Alc1, data = dataset)
# anova table
anova(fit11a, fit11b)
## Analysis of Variance Table
##
## Model 1: NormCounts2 ~ poly(Olaparib, 3) * BRCA * Alc1
## Model 2: NormCounts2 ~ poly(Olaparib, 3) * BRCA + Alc1
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 48 0.12684
## 2 55 0.23516 -7 -0.10831 5.8554 6.031e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit11c <- lm(NormCounts2 ~ poly(Olaparib,3)*Alc1*BRCA, data = dataset)
fit11d <- lm(NormCounts2 ~ poly(Olaparib,3)*Alc1+BRCA, data = dataset)
# anova table
anova(fit11c, fit11d)
## Analysis of Variance Table
##
## Model 1: NormCounts2 ~ poly(Olaparib, 3) * Alc1 * BRCA
## Model 2: NormCounts2 ~ poly(Olaparib, 3) * Alc1 + BRCA
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 48 0.12684
## 2 55 0.50014 -7 -0.37329 20.18 2.527e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
New analysis
- fit model for groups (BRCA+Alc1)
dataset$GSID <- factor(dataset$GSID, levels = unique(dataset$GSID))
GSID_levels <- levels(dataset$GSID)
for(g in seq_along(GSID_levels)){
cat("Group: ", GSID_levels[g], "\n")
dataset$GSID <- factor(dataset$GSID, levels = unique(dataset$GSID))
dataset$GSID <- relevel(dataset$GSID, ref = GSID_levels[g])
fit_group <- lm(NormCounts2 ~ poly(Olaparib,3)*GSID, data = dataset)
print(summary(fit_group))
cat("AIC: ", AIC(fit_group))
simres <- simulateResiduals(fittedModel = fit_group)
plot(simres)
if(g == 1){
output <- coef(summary(fit_group))
output <- output[grep("Olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output))
rownames(output) <- gsub("GSID", paste0(" ",GSID_levels[g], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$GSID)[1], sep = " in " )
} else {
outtmp <- coef(summary(fit_group))
outtmp <- outtmp[grep("Olaparib", rownames(outtmp)),]
rownames(outtmp) <- gsub("poly\\(|, [1-3]\\)","", rownames(outtmp))
rownames(outtmp) <- gsub("GSID", paste0(" ",GSID_levels[g], " vs. "), rownames(outtmp))
rownames(outtmp)[!(grepl("vs", rownames(outtmp)))] <- paste(rownames(outtmp)[!(grepl("vs", rownames(outtmp)))], levels(dataset$GSID)[1], sep = " in " )
output <- rbind(output,outtmp)
}
if(g < length(GSID_levels)){
output <- rbind(output, " ", colnames(output))
}
}
## Group: GFP h/d11
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.102304 -0.035893 -0.000865 0.029825 0.114226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.78917 0.01285 61.407 < 2e-16
## poly(Olaparib, 3)1 -1.42239 0.10281 -13.835 < 2e-16
## poly(Olaparib, 3)2 -0.30160 0.10281 -2.933 0.005126
## poly(Olaparib, 3)3 0.33655 0.10281 3.273 0.001974
## GSIDGFP-ALC1 h/d11 0.06641 0.01817 3.654 0.000639
## GSIDGFP d11/d11 -0.19921 0.01817 -10.961 1.16e-14
## GSIDGFP-ALC1 d11/d11 -0.08400 0.01817 -4.622 2.88e-05
## poly(Olaparib, 3)1:GSIDGFP-ALC1 h/d11 0.53637 0.14540 3.689 0.000574
## poly(Olaparib, 3)2:GSIDGFP-ALC1 h/d11 0.16580 0.14540 1.140 0.259820
## poly(Olaparib, 3)3:GSIDGFP-ALC1 h/d11 -0.32837 0.14540 -2.258 0.028500
## poly(Olaparib, 3)1:GSIDGFP d11/d11 -1.18566 0.14540 -8.155 1.29e-10
## poly(Olaparib, 3)2:GSIDGFP d11/d11 -0.17696 0.14540 -1.217 0.229537
## poly(Olaparib, 3)3:GSIDGFP d11/d11 -0.14057 0.14540 -0.967 0.338503
## poly(Olaparib, 3)1:GSIDGFP-ALC1 d11/d11 -0.58264 0.14540 -4.007 0.000213
## poly(Olaparib, 3)2:GSIDGFP-ALC1 d11/d11 -0.21731 0.14540 -1.495 0.141577
## poly(Olaparib, 3)3:GSIDGFP-ALC1 d11/d11 -0.09529 0.14540 -0.655 0.515355
##
## (Intercept) ***
## poly(Olaparib, 3)1 ***
## poly(Olaparib, 3)2 **
## poly(Olaparib, 3)3 **
## GSIDGFP-ALC1 h/d11 ***
## GSIDGFP d11/d11 ***
## GSIDGFP-ALC1 d11/d11 ***
## poly(Olaparib, 3)1:GSIDGFP-ALC1 h/d11 ***
## poly(Olaparib, 3)2:GSIDGFP-ALC1 h/d11
## poly(Olaparib, 3)3:GSIDGFP-ALC1 h/d11 *
## poly(Olaparib, 3)1:GSIDGFP d11/d11 ***
## poly(Olaparib, 3)2:GSIDGFP d11/d11
## poly(Olaparib, 3)3:GSIDGFP d11/d11
## poly(Olaparib, 3)1:GSIDGFP-ALC1 d11/d11 ***
## poly(Olaparib, 3)2:GSIDGFP-ALC1 d11/d11
## poly(Olaparib, 3)3:GSIDGFP-ALC1 d11/d11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05141 on 48 degrees of freedom
## Multiple R-squared: 0.971, Adjusted R-squared: 0.9619
## F-statistic: 107 on 15 and 48 DF, p-value: < 2.2e-16
##
## AIC: -182.6914

## Group: GFP-ALC1 h/d11
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.102304 -0.035893 -0.000865 0.029825 0.114226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.855575 0.012852 66.574 < 2e-16
## poly(Olaparib, 3)1 -0.886018 0.102812 -8.618 2.61e-11
## poly(Olaparib, 3)2 -0.135798 0.102812 -1.321 0.192819
## poly(Olaparib, 3)3 0.008178 0.102812 0.080 0.936928
## GSIDGFP h/d11 -0.066407 0.018175 -3.654 0.000639
## GSIDGFP d11/d11 -0.265613 0.018175 -14.614 < 2e-16
## GSIDGFP-ALC1 d11/d11 -0.150409 0.018175 -8.276 8.46e-11
## poly(Olaparib, 3)1:GSIDGFP h/d11 -0.536373 0.145398 -3.689 0.000574
## poly(Olaparib, 3)2:GSIDGFP h/d11 -0.165798 0.145398 -1.140 0.259820
## poly(Olaparib, 3)3:GSIDGFP h/d11 0.328374 0.145398 2.258 0.028500
## poly(Olaparib, 3)1:GSIDGFP d11/d11 -1.722032 0.145398 -11.844 7.50e-16
## poly(Olaparib, 3)2:GSIDGFP d11/d11 -0.342754 0.145398 -2.357 0.022532
## poly(Olaparib, 3)3:GSIDGFP d11/d11 0.187807 0.145398 1.292 0.202657
## poly(Olaparib, 3)1:GSIDGFP-ALC1 d11/d11 -1.119017 0.145398 -7.696 6.36e-10
## poly(Olaparib, 3)2:GSIDGFP-ALC1 d11/d11 -0.383103 0.145398 -2.635 0.011299
## poly(Olaparib, 3)3:GSIDGFP-ALC1 d11/d11 0.233083 0.145398 1.603 0.115481
##
## (Intercept) ***
## poly(Olaparib, 3)1 ***
## poly(Olaparib, 3)2
## poly(Olaparib, 3)3
## GSIDGFP h/d11 ***
## GSIDGFP d11/d11 ***
## GSIDGFP-ALC1 d11/d11 ***
## poly(Olaparib, 3)1:GSIDGFP h/d11 ***
## poly(Olaparib, 3)2:GSIDGFP h/d11
## poly(Olaparib, 3)3:GSIDGFP h/d11 *
## poly(Olaparib, 3)1:GSIDGFP d11/d11 ***
## poly(Olaparib, 3)2:GSIDGFP d11/d11 *
## poly(Olaparib, 3)3:GSIDGFP d11/d11
## poly(Olaparib, 3)1:GSIDGFP-ALC1 d11/d11 ***
## poly(Olaparib, 3)2:GSIDGFP-ALC1 d11/d11 *
## poly(Olaparib, 3)3:GSIDGFP-ALC1 d11/d11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05141 on 48 degrees of freedom
## Multiple R-squared: 0.971, Adjusted R-squared: 0.9619
## F-statistic: 107 on 15 and 48 DF, p-value: < 2.2e-16
##
## AIC: -182.6914

## Group: GFP d11/d11
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.102304 -0.035893 -0.000865 0.029825 0.114226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.58996 0.01285 45.906 < 2e-16
## poly(Olaparib, 3)1 -2.60805 0.10281 -25.367 < 2e-16
## poly(Olaparib, 3)2 -0.47855 0.10281 -4.655 2.59e-05
## poly(Olaparib, 3)3 0.19598 0.10281 1.906 0.062616
## GSIDGFP h/d11 0.19921 0.01817 10.961 1.16e-14
## GSIDGFP-ALC1 h/d11 0.26561 0.01817 14.614 < 2e-16
## GSIDGFP-ALC1 d11/d11 0.11520 0.01817 6.339 7.60e-08
## poly(Olaparib, 3)1:GSIDGFP h/d11 1.18566 0.14540 8.155 1.29e-10
## poly(Olaparib, 3)2:GSIDGFP h/d11 0.17696 0.14540 1.217 0.229537
## poly(Olaparib, 3)3:GSIDGFP h/d11 0.14057 0.14540 0.967 0.338503
## poly(Olaparib, 3)1:GSIDGFP-ALC1 h/d11 1.72203 0.14540 11.844 7.50e-16
## poly(Olaparib, 3)2:GSIDGFP-ALC1 h/d11 0.34275 0.14540 2.357 0.022532
## poly(Olaparib, 3)3:GSIDGFP-ALC1 h/d11 -0.18781 0.14540 -1.292 0.202657
## poly(Olaparib, 3)1:GSIDGFP-ALC1 d11/d11 0.60302 0.14540 4.147 0.000136
## poly(Olaparib, 3)2:GSIDGFP-ALC1 d11/d11 -0.04035 0.14540 -0.278 0.782582
## poly(Olaparib, 3)3:GSIDGFP-ALC1 d11/d11 0.04528 0.14540 0.311 0.756847
##
## (Intercept) ***
## poly(Olaparib, 3)1 ***
## poly(Olaparib, 3)2 ***
## poly(Olaparib, 3)3 .
## GSIDGFP h/d11 ***
## GSIDGFP-ALC1 h/d11 ***
## GSIDGFP-ALC1 d11/d11 ***
## poly(Olaparib, 3)1:GSIDGFP h/d11 ***
## poly(Olaparib, 3)2:GSIDGFP h/d11
## poly(Olaparib, 3)3:GSIDGFP h/d11
## poly(Olaparib, 3)1:GSIDGFP-ALC1 h/d11 ***
## poly(Olaparib, 3)2:GSIDGFP-ALC1 h/d11 *
## poly(Olaparib, 3)3:GSIDGFP-ALC1 h/d11
## poly(Olaparib, 3)1:GSIDGFP-ALC1 d11/d11 ***
## poly(Olaparib, 3)2:GSIDGFP-ALC1 d11/d11
## poly(Olaparib, 3)3:GSIDGFP-ALC1 d11/d11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05141 on 48 degrees of freedom
## Multiple R-squared: 0.971, Adjusted R-squared: 0.9619
## F-statistic: 107 on 15 and 48 DF, p-value: < 2.2e-16
##
## AIC: -182.6914

## Group: GFP-ALC1 d11/d11
##
## Call:
## lm(formula = NormCounts2 ~ poly(Olaparib, 3) * GSID, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.102304 -0.035893 -0.000865 0.029825 0.114226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.70517 0.01285 54.870 < 2e-16 ***
## poly(Olaparib, 3)1 -2.00503 0.10281 -19.502 < 2e-16 ***
## poly(Olaparib, 3)2 -0.51890 0.10281 -5.047 6.87e-06 ***
## poly(Olaparib, 3)3 0.24126 0.10281 2.347 0.023119 *
## GSIDGFP h/d11 0.08400 0.01817 4.622 2.88e-05 ***
## GSIDGFP-ALC1 h/d11 0.15041 0.01817 8.276 8.46e-11 ***
## GSIDGFP d11/d11 -0.11520 0.01817 -6.339 7.60e-08 ***
## poly(Olaparib, 3)1:GSIDGFP h/d11 0.58264 0.14540 4.007 0.000213 ***
## poly(Olaparib, 3)2:GSIDGFP h/d11 0.21731 0.14540 1.495 0.141577
## poly(Olaparib, 3)3:GSIDGFP h/d11 0.09529 0.14540 0.655 0.515355
## poly(Olaparib, 3)1:GSIDGFP-ALC1 h/d11 1.11902 0.14540 7.696 6.36e-10 ***
## poly(Olaparib, 3)2:GSIDGFP-ALC1 h/d11 0.38310 0.14540 2.635 0.011299 *
## poly(Olaparib, 3)3:GSIDGFP-ALC1 h/d11 -0.23308 0.14540 -1.603 0.115481
## poly(Olaparib, 3)1:GSIDGFP d11/d11 -0.60302 0.14540 -4.147 0.000136 ***
## poly(Olaparib, 3)2:GSIDGFP d11/d11 0.04035 0.14540 0.278 0.782582
## poly(Olaparib, 3)3:GSIDGFP d11/d11 -0.04528 0.14540 -0.311 0.756847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05141 on 48 degrees of freedom
## Multiple R-squared: 0.971, Adjusted R-squared: 0.9619
## F-statistic: 107 on 15 and 48 DF, p-value: < 2.2e-16
##
## AIC: -182.6914

# suggested result table
kable(output, row.names = T)
| Olaparib1 in GFP h/d11 |
-1.42239095274189 |
0.102812155919594 |
-13.8348519201785 |
2.2707499059511e-18 |
| Olaparib2 in GFP h/d11 |
-0.301595542580835 |
0.102812155919594 |
-2.93346190324714 |
0.00512637859781538 |
| Olaparib3 in GFP h/d11 |
0.336552195706758 |
0.102812155919594 |
3.27346696211647 |
0.00197387783090667 |
| Olaparib1: GFP h/d11 vs. GFP-ALC1 h/d11 |
0.536372847144384 |
0.145398345278307 |
3.68898866158147 |
0.000573740887224468 |
| Olaparib2: GFP h/d11 vs. GFP-ALC1 h/d11 |
0.165797594232795 |
0.145398345278307 |
1.14029904477552 |
0.259819677419695 |
| Olaparib3: GFP h/d11 vs. GFP-ALC1 h/d11 |
-0.328373721544222 |
0.145398345278307 |
-2.258441943859 |
0.0285002802015001 |
| Olaparib1: GFP h/d11 vs. GFP d11/d11 |
-1.18565923135235 |
0.145398345278307 |
-8.15455794275291 |
1.28746356096237e-10 |
| Olaparib2: GFP h/d11 vs. GFP d11/d11 |
-0.176956335214119 |
0.145398345278307 |
-1.21704504184973 |
0.229537168947121 |
| Olaparib3: GFP h/d11 vs. GFP d11/d11 |
-0.140567213608647 |
0.145398345278307 |
-0.966773131699587 |
0.338503477328255 |
| Olaparib1: GFP h/d11 vs. GFP-ALC1 d11/d11 |
-0.58264402262155 |
0.145398345278307 |
-4.00722595230578 |
0.000213083271958141 |
| Olaparib2: GFP h/d11 vs. GFP-ALC1 d11/d11 |
-0.217305434366553 |
0.145398345278307 |
-1.49455232073383 |
0.141576888983454 |
| Olaparib3: GFP h/d11 vs. GFP-ALC1 d11/d11 |
-0.0952905489309342 |
0.145398345278307 |
-0.655375745497916 |
0.515354765100382 |
|
|
|
|
|
|
Estimate |
Std. Error |
t value |
Pr(>|t|) |
| Olaparib1 in GFP-ALC1 h/d11 |
-0.886018105597509 |
0.102812155919594 |
-8.61783412352945 |
2.60751076037414e-11 |
| Olaparib2 in GFP-ALC1 h/d11 |
-0.13579794834804 |
0.102812155919594 |
-1.32083552896452 |
0.192818984629593 |
| Olaparib3 in GFP-ALC1 h/d11 |
0.00817847416253651 |
0.102812155919594 |
0.0795477352788189 |
0.936927688494952 |
| Olaparib1: GFP-ALC1 h/d11 vs. GFP h/d11 |
-0.536372847144383 |
0.145398345278307 |
-3.68898866158147 |
0.000573740887224478 |
| Olaparib2: GFP-ALC1 h/d11 vs. GFP h/d11 |
-0.165797594232795 |
0.145398345278307 |
-1.14029904477552 |
0.259819677419696 |
| Olaparib3: GFP-ALC1 h/d11 vs. GFP h/d11 |
0.328373721544222 |
0.145398345278307 |
2.258441943859 |
0.0285002802015002 |
| Olaparib1: GFP-ALC1 h/d11 vs. GFP d11/d11 |
-1.72203207849673 |
0.145398345278307 |
-11.8435466043344 |
7.50077020421532e-16 |
| Olaparib2: GFP-ALC1 h/d11 vs. GFP d11/d11 |
-0.342753929446914 |
0.145398345278307 |
-2.35734408662525 |
0.0225315755535322 |
| Olaparib3: GFP-ALC1 h/d11 vs. GFP d11/d11 |
0.187806507935575 |
0.145398345278307 |
1.29166881215941 |
0.202657021722137 |
| Olaparib1: GFP-ALC1 h/d11 vs. GFP-ALC1 d11/d11 |
-1.11901686976593 |
0.145398345278307 |
-7.69621461388725 |
6.35606597996494e-10 |
| Olaparib2: GFP-ALC1 h/d11 vs. GFP-ALC1 d11/d11 |
-0.383103028599348 |
0.145398345278307 |
-2.63485136550935 |
0.0112991065251505 |
| Olaparib3: GFP-ALC1 h/d11 vs. GFP-ALC1 d11/d11 |
0.233083172613288 |
0.145398345278307 |
1.60306619836108 |
0.115480983067289 |
|
|
|
|
|
|
Estimate |
Std. Error |
t value |
Pr(>|t|) |
| Olaparib1 in GFP d11/d11 |
-2.60805018409424 |
0.102812155919594 |
-25.367138357977 |
1.85753198086583e-29 |
| Olaparib2 in GFP d11/d11 |
-0.478551877794954 |
0.102812155919594 |
-4.65462350744996 |
2.58529451459942e-05 |
| Olaparib3 in GFP d11/d11 |
0.195984982098112 |
0.102812155919594 |
1.90624328752901 |
0.0626155147386495 |
| Olaparib1: GFP d11/d11 vs. GFP h/d11 |
1.18565923135235 |
0.145398345278307 |
8.15455794275292 |
1.28746356096233e-10 |
| Olaparib2: GFP d11/d11 vs. GFP h/d11 |
0.176956335214118 |
0.145398345278307 |
1.21704504184972 |
0.229537168947123 |
| Olaparib3: GFP d11/d11 vs. GFP h/d11 |
0.140567213608647 |
0.145398345278307 |
0.96677313169959 |
0.338503477328253 |
| Olaparib1: GFP d11/d11 vs. GFP-ALC1 h/d11 |
1.72203207849673 |
0.145398345278307 |
11.8435466043344 |
7.50077020421522e-16 |
| Olaparib2: GFP d11/d11 vs. GFP-ALC1 h/d11 |
0.342753929446914 |
0.145398345278307 |
2.35734408662525 |
0.0225315755535321 |
| Olaparib3: GFP d11/d11 vs. GFP-ALC1 h/d11 |
-0.187806507935575 |
0.145398345278307 |
-1.29166881215941 |
0.202657021722137 |
| Olaparib1: GFP d11/d11 vs. GFP-ALC1 d11/d11 |
0.603015208730802 |
0.145398345278307 |
4.14733199044713 |
0.000136325781575899 |
| Olaparib2: GFP d11/d11 vs. GFP-ALC1 d11/d11 |
-0.0403490991524347 |
0.145398345278307 |
-0.277507278884106 |
0.782582359632607 |
| Olaparib3: GFP d11/d11 vs. GFP-ALC1 d11/d11 |
0.045276664677713 |
0.145398345278307 |
0.311397386201671 |
0.756847016986467 |
|
|
|
|
|
|
Estimate |
Std. Error |
t value |
Pr(>|t|) |
| Olaparib1 in GFP-ALC1 d11/d11 |
-2.00503497536344 |
0.102812155919594 |
-19.5019252094228 |
1.8663910348478e-24 |
| Olaparib2 in GFP-ALC1 d11/d11 |
-0.518900976947388 |
0.102812155919594 |
-5.04707806490512 |
6.86600762767975e-06 |
| Olaparib3 in GFP-ALC1 d11/d11 |
0.241261646775825 |
0.102812155919594 |
2.34662569438294 |
0.0231193755416832 |
| Olaparib1: GFP-ALC1 d11/d11 vs. GFP h/d11 |
0.58264402262155 |
0.145398345278307 |
4.00722595230578 |
0.000213083271958141 |
| Olaparib2: GFP-ALC1 d11/d11 vs. GFP h/d11 |
0.217305434366553 |
0.145398345278307 |
1.49455232073383 |
0.141576888983453 |
| Olaparib3: GFP-ALC1 d11/d11 vs. GFP h/d11 |
0.0952905489309342 |
0.145398345278307 |
0.655375745497917 |
0.515354765100381 |
| Olaparib1: GFP-ALC1 d11/d11 vs. GFP-ALC1 h/d11 |
1.11901686976593 |
0.145398345278307 |
7.69621461388724 |
6.35606597996499e-10 |
| Olaparib2: GFP-ALC1 d11/d11 vs. GFP-ALC1 h/d11 |
0.383103028599349 |
0.145398345278307 |
2.63485136550936 |
0.0112991065251503 |
| Olaparib3: GFP-ALC1 d11/d11 vs. GFP-ALC1 h/d11 |
-0.233083172613288 |
0.145398345278307 |
-1.60306619836108 |
0.115480983067289 |
| Olaparib1: GFP-ALC1 d11/d11 vs. GFP d11/d11 |
-0.603015208730801 |
0.145398345278307 |
-4.14733199044713 |
0.000136325781575902 |
| Olaparib2: GFP-ALC1 d11/d11 vs. GFP d11/d11 |
0.0403490991524344 |
0.145398345278307 |
0.277507278884104 |
0.782582359632609 |
| Olaparib3: GFP-ALC1 d11/d11 vs. GFP d11/d11 |
-0.0452766646777127 |
0.145398345278307 |
-0.311397386201669 |
0.756847016986469 |
write.table(output, file = "Figure5H_Stats_New_All.txt", quote = F, sep = "\t", row.names = T, col.names = NA)